Collaborative navigation allows two or more navigation platforms to work together by sharing information to produce a navigation solution that is better than if each navigation platform acted independently. For example, many autonomous unmanned aerial vehicles can operate and navigate in an environment for surveillance, remote sensing, or rescue. In a collaborative navigation system, these vehicles can cooperate to improve navigation by sharing information.
Communication between two moving navigation platforms is typically available sometime during collaborative navigation. The communication channels between the platforms are a necessary component of a collaborative navigation system as they allow the platforms to share information. The information of primary concern in collaborative navigation is the probabilistic dependence that arises between the platforms in the collaboration network. Collaborative navigation algorithms must account for this dependence, which arises in a number of ways. One example is when two platforms range to each other. Such a relative range measurement makes an observation of the position of both platforms simultaneously. As a result of one or more measurements like this, the navigation solutions of both platforms are probabilistically dependent. Another example is the multi-vehicle Simultaneous Localization and Mapping (SLAM) situation where each mobile platform observes landmark states and one or more of the landmarks is observed by each mobile platform. This observation of a common exogenous state also creates probabilistic dependence in the navigation solutions of the mobile platforms.
In collaborative navigation, a platform uses information from its own sensors, as well as information from other platforms, to enhance its own navigation solution. Since all platforms in the collaborative network play a similar role, they may all reap the benefit of an enhanced navigation solution. The SLAM method is one way for the navigation, that is, the information obtained by a sensor on a mobile platform is processed to obtain an estimate of its own position while building a map of the environment.
Previous methods for collaborative navigation have used a global centralized filter that includes all the navigation states for each platform, the dynamic state model for each platform, the measurement model of the sensors, and the landmarks (when using SLAM techniques). The disadvantage of a global centralized filter is that each platform must maintain estimates of all the states of all platforms, which means the centralized filter must have knowledge of all dynamic and measurement models of all platforms in the network. This is additional information that must be transmitted and shared at regular intervals, which means that centralized approaches are vulnerable to communication disruptions. Additionally, with large numbers of platforms collaborating, the size of the centralized state vector may result in a solution that is computationally impractical.